Search Engineer (Software Engineer III)

We’re transforming how millions of industrial buyers discover products through intelligent search experiences powered by AI, machine learning, and modern search technologies.

As a Full Stack Engineer III on our AI Search & Product Discovery team, you’ll build the frontend applications, backend services, and search infrastructure that power highly relevant, personalized search experiences at scale. You’ll work across the full development lifecycle, from rapid prototyping and experimentation to deploying production-grade solutions and continuously optimizing performance based on real user behavior.

This is an ideal opportunity for an engineer who enjoys solving complex problems at the intersection of full-stack development, search engineering, cloud-native architecture, machine learning, and AI-driven user experiences.

What You’ll Do

Build AI-Powered Search Experiences

• Design, develop, and deploy scalable Python services that power search retrieval, ranking, recommendation, and discovery experiences.
• Build and integrate machine learning inference pipelines, including embeddings, transformer models, query understanding, reranking services, and LLM-powered features.
• Develop robust APIs and microservices that support high-volume search workloads and customer-facing applications.
• Collaborate with product, engineering, and AI teams to translate business goals into impactful search solutions.
• Continuously improve search relevance, performance, and user engagement through experimentation and data-driven decision making.

Develop Modern Full-Stack Applications

• Build responsive, accessible, and performant user interfaces using React and modern JavaScript frameworks.
• Create reusable frontend components and establish engineering standards that improve consistency and developer productivity.
• Partner with UX and Product teams to transform wireframes and Figma designs into intuitive customer experiences.
• Contribute to frontend architecture decisions that support long-term scalability and maintainability.

Advance Search & AI Infrastructure

• Implement hybrid search architectures that combine keyword search, vector search, semantic retrieval, and AI-powered ranking.
• Build and maintain Elasticsearch/OpenSearch indexing pipelines, query services, and relevance tuning capabilities.
• Integrate vector databases and retrieval systems such as Pinecone, Weaviate, FAISS, or similar technologies.
• Develop and optimize Retrieval-Augmented Generation (RAG) and LLM-powered search workflows.
• Instrument search systems with meaningful metrics, including click-through rate, latency, engagement, and zero-result rates to drive ongoing optimization.

Build Cloud-Native Systems

• Develop event-driven, distributed systems using Google Cloud Platform (GCP) services such as Cloud Run, GKE, Pub/Sub, and Cloud Functions.
• Deploy, monitor, and maintain production services using modern DevOps and observability practices.
• Own service reliability through testing, monitoring, troubleshooting, and operational excellence.

Contribute to Engineering Excellence

• Participate in architecture discussions, technical design reviews, and code reviews.
• Mentor peers through collaboration and knowledge sharing.
• Champion engineering best practices, software craftsmanship, and continuous improvement.

• 4+ years of professional experience in Full Stack Engineering, Backend Engineering, or Software Development.
• Strong hands-on experience building applications with Python and React.
• Experience with modern frontend frameworks such as Next.js, Remix, Vite, Gatsby, or similar technologies.
• Proven experience building and supporting scalable microservices, REST APIs, and/or gRPC services.
• Experience deploying and operating cloud-native applications in GCP, AWS, or Azure.
• Experience with Docker, containerized applications, and serverless architectures.
• Strong understanding of software design patterns, SOLID principles, testing strategies, and maintainable code practices.
• Experience working with relational and NoSQL databases such as PostgreSQL, MySQL, Oracle, MongoDB, DynamoDB, or similar platforms.
• Strong communication skills with the ability to collaborate effectively across engineering, product, and architecture teams.
• Comfortable leveraging AI-assisted development tools to improve productivity and quality.